张莘琛, 祝一诺, 叶爱中. 1985—2020年太湖月尺度高精度水体面积提取分析[J]. 北京师范大学学报(自然科学版). DOI: 10.12202/j.0476-0301.2023057
引用本文: 张莘琛, 祝一诺, 叶爱中. 1985—2020年太湖月尺度高精度水体面积提取分析[J]. 北京师范大学学报(自然科学版). DOI: 10.12202/j.0476-0301.2023057
ZHANG Xinchen, ZHU Yinuo, YE Aizhong. Extraction of high-precision water area at the monthly scale of Lake Taihu in 1985-2020[J]. Journal of Beijing Normal University(Natural Science). DOI: 10.12202/j.0476-0301.2023057
Citation: ZHANG Xinchen, ZHU Yinuo, YE Aizhong. Extraction of high-precision water area at the monthly scale of Lake Taihu in 1985-2020[J]. Journal of Beijing Normal University(Natural Science). DOI: 10.12202/j.0476-0301.2023057

1985—2020年太湖月尺度高精度水体面积提取分析

Extraction of high-precision water area at the monthly scale of Lake Taihu in 1985-2020

  • 摘要: 选取太湖作为研究对象,使用长期高分辨率水体掩膜数据集,通过水体分类增强算法纠正数据中受到云、云阴影、地形阴影等污染的像素.本研究提供了长达三十多年(1985—2020年)的太湖月尺度水覆盖图和水体面积数据,其中水体面积平均值为2 421.00 km2,数据方差较算法纠正前锐减,平均面积增强率达20.76%,验证结果可靠.这些数据可以用来支持水资源分析和科学管理,并为维护该区域的生态平衡和持续利用水资源提供科学依据和数据支撑.

     

    Abstract: As an important resource for human survival and development, water resources play an important role in maintaining the health and stability of ecosystems and sustainable social and economic development of mankind. With the continuous progress and development of remote sensing technology, it is of great significance to carry out dynamic monitoring and analysis of water bodies through remote sensing means for the scientific management and sustainable development of water resources. However, due to the influence of clouds, cloud shadows, etc., it is difficult to obtain water area data with high temporal resolution. In this paper, Taihu Lake is selected as the research object, and the long-term high-resolution water mask dataset is used to correct the pixels polluted by clouds, cloud shadows, and terrain shadows in the data through the water classification enhancement algorithm. This study provides a monthly scale water cover map and water area data of Taihu Lake for more than 30 years (1985-2020), of which the average water area is 2421.00 km2, the data variance is sharply reduced compared with the algorithm before correction, and the average area enhancement rate is 20.76%, which is reliable. These data can be used to support water analysis and scientific management, and provide scientific basis and data support for maintaining the ecological balance and sustainable use of water resources in the region.

     

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